Spatial autocorrelation dimension as a potential determinant for the temporal persistence of human atrial and ventricular fibrillation
Bibliographic record
Abstract
ABSTRACT Background: Despite being central to atrial fibrillation (AF) and ventricular fibrillation (VF) mechanisms and therapy, the factors governing AF and VF termination are poorly understood. It has been noted that ratio of system size ( L ) and the two-point spatial correlation length (ξ 2 ) are associated with time until termination in transient spatiotemporally chaotic systems, but the relationship between these characteristics and termination has not been systematically studied in human AF and VF. Objective: We aimed assess whether the time to cardiac fibrillation termination can be predicted using a novel estimator, the spatial autocorrelation dimension ( D i ), defined as the ratio of L and ξ 2 , in human AF and VF. Methods: D i was computed and compared in a multi-centre, multi-system study with data for sustained versus spontaneously terminating human AF/VF. VF data was collected during coronary-bypass surgery; and AF data during clinically indicated AF ablation. We analyzed: i) VF mapped using a 256-electrode epicardial sock (n=12pts); ii) AF mapped using a 64-electrode constellation basket-catheter (n=15pts); iii) AF mapped using a 16-electrode HD-grid catheter (n=42pts). To investigate temporal fibrillation persistence, the response of AF-episodes to flecainide (n=7pts) was also studied. Results: Spontaneously terminating fibrillation demonstrated a lower D i (P<0.001 all systems). Lower D i was also seen in paroxysmal compared to persistent AF (P=0.002). Post-flecainide, D i decreased over time (P<0.001). Lower D i was also associated with longer-lasting episodes of AF/VF (R 2 >0.90, P<0.05 in all cases). Using k-means clustering, two distinct clusters and their centroids were identified i) a cluster of spontaneously terminating episodes, and ii) a cluster of sustained epochs. Conclusion: D i predicts the temporal persistence of cardiac fibrillation. This finding provides potentially important insights into a possible common pathway to termination and therapeutic approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".